Machine Learning

10-601, Spring 2015

Carnegie Mellon University

Tom Mitchell and Maria-Florina Balcan

Course Project Guidelines

Your class project is an opportunity for you to explore an interesting machine learning problem of your choice in the context of a real-world data set. Below, you will find some project ideas, but the best idea would be to combine machine learning with problems in your own research area. Your class project must be about new things you have done this semester; you can't use results you have developed in previous semesters. In short, a typical project consists of picking an interesting dataset, applying one or more appropriate and well-known machine learning techniques for an interesting task as baselines, and extending these baselines in creative and interesting ways.
  • Projects can be done in teams of 2-4 students. Team members are responsible for dividing up the work equally and making sure that each member contributes.
  • If you are having trouble writing a proposal, feel free to consult with a TA or the instructor. Once you submit your proposal, we will give you feedback. Of course the final responsibility to define and execute an interesting piece of work is yours
  • Each project will be assigned a 10601 TA as a project consultant/mentor.
  • You are strongly urged to consult a TA or the instructor early on if your project will rely purely on simulated data or if you intend to do a learning theory related project. A theory project should look at a concrete technical open question, analyze relevant literature, and develop insightful and interesting attempts. Links to open problems can be found here.
Your project will be worth 20% of your final class grade, and will have 4 deliverables:
  • Proposal, March 6: 1 page (10%)
  • Midway Report, April 6: 5-8 pages (25%)
  • Poster Presentation, May 11: (20%)
  • Final Report, May 8: 10 pages (45%)
Note that all write-ups must be in the form of a NIPS paper. The page limits are strict! Papers over the limit will not be considered. Each deliverable of your project will be evaluated based on several factors:
  • The novelty of the project ideas and applications. The groups are encouraged to come up with original ideas and novel applications for the projects. A project with new ideas (algorithms, methods, theory) on ML or new, interesting applications of existing algorithms is scored higher than a project without much new idea/application.
  • The extensiveness of the study and experiments. A project that produces a more intelligent system by combining several ML techniques together, or a project that involves well-designed experiments and thorough analysis of the experimental results, or a project that nicely incorporates various real world applications, are scored higher.
  • The writing style and the clarity of the written paper.

Project Proposal (Due Date: March 6)

A list of suggested projects and data sets are posted below. Read the list carefully. You are encouraged to use one of the suggested data sets, because we know that they have been successfully used for machine learning in the past. If you prefer to use a different data set, we will consider your proposal, but (1) you must discuss your idea with an instructor or a TA before submitting the proposal and (2) you must have access to this data already, and present a clear proposal for what you would do with it.

Page limit: Proposals should be one page maximum.

Include the following information:
  • Project title
  • Data set
  • Project idea. This should be approximately two paragraphs.
  • Software you will need to write.
  • Papers to read.Include 1-3 relevant papers.You will probably want to read at least one of them before submitting your proposal
  • Teammate (if any) and work division. We expect projects done in a group to be more substantial than projects done individually.
  • Midterm milestone: What will you complete by April 6? Specifically, what baselines will you try on your datasets and what kind of results do you expect to report ?

Midway Report (Due Date: April 6)

This should be a 5-8 pages report in the form of a NIPS paper, and it serves as a check-point. By this date you should have applied one or more commonly used learning methods on a specific problem. The report should include a background section which describes the problem you are tackling, a related work section, a description of the method(s) you tried, an experiments section with detailed description of the dataset you experimented with and the results you obtained. For a theory project, instead of the experiments section, the report must include an insightful analysis of 2-3 related papers. The analysis should demonstrate your deep understanding of these papers.

Final Report (Due Date: May 8)

The structure of the final report is similar to that of the midterm report. By that time you should extend the baseline(s) you tried in an interesting and creative way. That can include feature engineering, optimizing the training algorithm, modifications to the model ... etc. For a theory project, you should present interesting attempts to attack the problem at hand.

Poster Presentation (Date: May 11)

All project members should be present during the poster hours. The session will be open to everybody.

Here are some details on the poster format.
  • The two most common ways to make your poster are:
    • You can create a huge slide (in powerpoint, using beamer, etc) and print it out at one of the poster printers available (for SCS, details below). You'd want the shorter dimension to be less than 36'' since the special poster printers use paper rolls of width 36''.
    • You can create a bunch of "normal" presentation slides, print out each one on a piece of (letter-sized) paper, and put them all together on a poster board. This is somewhat messier (and not as pretty) but you don't need to wait for the special poster printer to print.
  • We will provide you with foam boards onto which you can tack on your poster (or your slides for the poster). The foam boards are 32'' by 40''.
  • Printing: Unfortunately, we don't have a budget to pay for printing. If you are an SCS student, SCS has a poster printer you can use which prints on a 36'' wide roll of paper. See SCS Poster Printing for more details
If you are a student outside SCS, you will need to check with your department to see if there are printing facilities for big posters (we're not sure what is offered outside SCS), or just print a collection of regular sized pages and attach them to the poster board.

Project Suggestions

Here are some project ideas and datasets:

Vector Space Models for Natural Language Processing

Many tasks in natural language processing can be performed with only very shallow understanding of text. The vector space model is one example of a useful, but shallow, data representation that has been successfully used for many tasks, including detecting synonyms, finding analogies, and learning properties of noun phrases. The vector space model represents the meaning of a noun phrase(NP) (e.g. "the New York Yankees" or "house") as a vector of co-occurrence counts with contexts. A context is a short snippet of text like "alex rodriguez plays for _" or "_ on the street". The model is essentially a (very) large matrix A, whose rows represent noun phrases and whose columns represent contexts. The value of entry A_{i,j} is the number of times noun phrase i occurred with context j in a large corpus of documents (e.g., the Web). Intuitively, this model contains useful information because some contexts only occur with certain types of noun phrases; for example, the context "athletes, such as _" only occurs with athletes.

Download Dataset and Related Tools

Project ideas:
In this project, we will provide you with an NP-context co-occurrence matrix and ask you to do something interesting with it. Possible applications include finding synonyms, finding members of categories (i.e., "is this noun phrase an athlete?"), or clustering noun phrases to automatically induce categories. For more ideas, we recommend reading (1).

Another interesting set of projects could use this data as a case study for learning in high-dimensions. These projects could examine how dimensionality reduction or regularization affect performance on one of the above tasks.

Vector space models are also used for many other tasks, including document classification and topic modeling. The 20 Newsgroups data is a classic data set for these tasks. You can obtain this dataset from here

(1) Peter D. Turney and Patrick Pantel (2010). From Frequency to Meaning: Vector Space Models of Semantics. Journal of Artificial Intelligence Research 37, pp. 141-188.

Semi-Supervised Learning

In many applications, it is easy to obtain a large amount of unlabeled data, but difficult or costly to label this data. Semi-supervised learning studies algorithms which learn from a small amount of labeled data and a large pool of unlabeled data. Interestingly, semi-supervised learning is not always successful, and unlabeled data points do not always improve performance. Semi-supervised learning algorithms typically make an assumption about the data distribution which enables learning -- for example, several algorithms assume that the decision boundary should not pass through regions with high data density. When this assumption is satisfied, the algorithms perform better than supervised learning.

The goal of this project is to experiment with semi-supervised learning algorithms on a data set of your choice. Some algorithms you can consider using are: co-training, self-training, transductive SVMS (S3VMs), or one of the many graph-based algorithms. (We recommend reading (1) for a survey of the many approaches to semi-supervised learning.) You may compare several semi-supervised and supervised algorithms on your data set, and perhaps draw some general conclusions about semi-supervised learning.

This project can use essentially any data set. For some ideas, we recommend consulting the UC Irvine Machine Learning Repository.

(1) Xiaojin Zhu. Semi-supervised Learning Literature Survey. Available Here.

Privacy-Related Project Ideas

A recent paper shows how decision trees may be approximated in the "secure multiparty" setting, by using tools of cryptography. This allows several parties to compute a decision tree on the union of their data without requiring them to share the data itself with each other. See here for the algorithm and here for a general discussion.

An alternative notion of privacy which has received much attention lately is so-called "Differential Privacy" (see e.g, here. So far approaches have been studied for many machine learning methods under this model of privacy (such as logistic regression: see here and svm: see here. This notion of privacy is fundamentally different to the usual cryptographic one.

The datasets for this project can be found at the UCI machine learning archive (Please consult Rob Hall for more details about the datasets.).

Project Idea 1: Differentially Private Decision Trees
See whether it is possible to implement a decision tree learner in a differentially-private way. This would entail creating a randomized algorithm which outputs a decision tree. Furthermore, when one of the elements of the input data is changed, the distribution over the outputs should not change by much (cf, the definition of differential privacy). Analyze under what conditions the approach will work and analyze the error rate relative to that of the non-private decision tree.

Project Idea 2: Secure Multiparty EM
Implement EM for some kind of mixture model in a secure multiparty way (i.e., making use of cryptography to protect the intermediate quantities). This has been studied already in the context of imputation (see here). Although in principle, existing cryptographic primitives may be used to compute any funciton, in order to get a reasonably efficient algorithm you will have to come up with approximations which are less expensive to compute. Analyze the effects of any such approximations you make.

Project Idea 3: Differentially Private Sparse Covariance Estimation
The estimation of gaussian covariance matrices has received ample attention lately (see e.g, here). When the covariance matrix is sparse (i.e., the off-diagonal elements are mostly 0) then there is a nice correspondence between the multivariate gaussian, and a certain type of undirected graphical model. See whether it is possible to estimate sparse covariance matrices in a way which satisfies the definition of differential privacy as shown above. Analyze how much error will be in the algorithm compared with the non-private one.

KDD cup 2010 (Provided by Datashop in Pittsburgh Science Learning Center)

The KDD Cup is the annual Data Mining and Knowledge Discovery competition in which some of the best data mining teams in the world compete to solve an practical data mining problem of some importance. The 2010 challenge was about discovering how generally or narrowly do students learn? How quickly or slowly? Will the rate of improvement vary between students? What does it mean for one problem to be similar to another? It might depend on whether the knowledge required for one problem is the same as the knowledge required for another. But is it possible to infer the knowledge requirements of problems directly from student performance data, without human analysis of the tasks? This year's challenge asks you to predict student performance on mathematical problems from logs of student interaction with Intelligent Tutoring Systems. This task presents interesting technical challenges, has practical importance, and is scientifically interesting.

[Download the datasets:] Click here to download the data.

More information about the datasets can be found by Click here.

You will apply the various machine learning techniques to the KDD datasets to improve the accuracy on predicting "Correct First Attempt values" for the test portion of the data. Please report the Root Mean Squared Error (RMSE).

30-40 Datasets for Education Data Mining

[Dataset Webpage:] Go to the webpage (You may need log in through WebISO) and click "Public Datasets".

There are about 30-40 public datasets available from the webpage (Only select the datasets whose status is labeled "complete"). If you clicking each datasets, you will find a general description and the related publications. To look at the datasets, click "Export" link.

Project Idea 1:
For each dataset, you can compare various machine learning techniques (at least five to seven different ML methods) on predicting "Correct First Attempt values" (Generally listed in the column "Outcome"). Please report the Root Mean Squared Error (RMSE).

Project Idea 2:
Across datasets, you can compare several machine learning techniques (at least two to three different ML methods) on predicting "Correct First Attempt values"(Generally listed in the column "Outcome"). Please report the Root Mean Squared Error (RMSE) on the test data. The hypothesis here is that there may not be an absolute winner, different machine learning techniques may be effective on different task domains. For example, you can split the datasets into science (physics & math) vs. second language learning (Chinese, French).

Image Segmentation Dataset
The goal is to segment images in a meaningful way. Berkeley collected three hundred images and paid students to hand-segment each one (usually each image has multiple hand-segmentations). Two-hundred of these images are training images, and the remaining 100 are test images. The dataset includes code for reading the images and ground-truth labels, computing the benchmark scores, and some other utility functions. It also includes code for a segmentation example. This dataset is new and the problem unsolved, so there is a chance that you could come up with the leading algorithm for your project.

Download Dataset

Project idea: Region-Based Segmentation
Most segmentation algorithms have focused on segmentation based on edges or based on discontinuity of color and texture. The ground-truth in this dataset, however, allows supervised learning algorithms to segment the images based on statistics calculated over regions. One way to do this is to "oversegment" the image into superpixels (Felzenszwalb 2004, code available) and merge the superpixels into larger segments. Graphical models can be used to represent smoothness in clusters, by adding appropriate potentials between neighboring pixels. In this project, you can address, for example, learning of such potentials, and inference in models with very large tree-width.

Character recognition (digits) data
Optical character recognition, and the simpler digit recognition task, has been the focus of much ML research. We have two datasets on this topic. The first tackles the more general OCR task, on a small vocabulary of words: (Note that the first letter of each word was removed, since these were capital letters that would make the task harder for you.)

Download dataset.

Project suggestion:
* Use an HMM to exploit correlations between neighboring letters in the general OCR case to improve accuracy. (Since ZIP codes don't have such constraints between neighboring digits, HMMs will probably not help in the digit case.)

NBA statistics data
This download contains 2004-2005 NBA and ABA stats for:

-Player regular season stats
-Player regular season career totals
-Player playoff stats
-Player playoff career totals
-Player all-star game stats
-Team regular season stats
-Complete draft history
-coaches_season.txt - nba coaching records by season
-coaches_career.txt - nba career coaching records
Currently all of the regular season

Project idea:
* outlier detection on the players; find out who are the outstanding players.
* predict the game outcome.

Precipitation data
This dataset has includes 45 years of daily precipitation data from the Northwest of the US:

Download Dataset

Project ideas:
Weather prediction: Learn a probabilistic model to predict rain levels.
Sensor selection: Where should you place sensor to best predict rain.

This dataset contains webpages from 4 universities, labeled with whether they are professor, student, project, or other pages.
Download Dataset.

Project ideas:
* Learning classifiers to predict the type of webpage from the text.
* Can you improve accuracy by exploiting correlations between pages that point to each other using graphical models?


Email Annotation
The datasets provided below are sets of emails. The goal is to identify which parts of the email refer to a person name. This task is an example of the general problem area of Information Extraction.

Download Dataset

Project Ideas:
* Model the task as a Sequential Labeling problem, where each email is a sequence of tokens, and each token can have either a label of "person-name" or "not-a-person-name".


Netflix Prize Dataset
The Netflix Prize data set gives 100 million records of the form "user X rated movie Y a 4.0 on 2/12/05". The data is available here: Netflix Prize.

Project idea:
  • Can you predict the rating a user will give on a movie from the movies that user has rated in the past, as well as the ratings similar users have given similar movies?
  • Can you discover clusters of similar movies or users?
  • Can you predict which users rated which movies in 2006? In other words, your task is to predict the probability that each pair was rated in 2006. Note that the actual rating is irrelevant, and we just want whether the movie was rated by that user sometime in 2006. The date in 2006 when the rating was given is also irrelevant. The test data can be found at this website.

Object Recognition
The Caltech 256 dataset contains images of 256 object categories taken at varying orientations, varying lighting conditions, and with different backgrounds.

Download Dataset.

Project ideas:
* You can try to create an object recognition system which can identify which object category is the best match for a given test image.
* Apply clustering to learn object categories without supervision.

Enron E-mail Dataset
The Enron E-mail data set contains about 500,000 e-mails from about 150 users.

The data set is available here

Project ideas:
* Can you classify the text of an e-mail message to decide who sent it?

More data
There are many other datasets out there. UC Irvine has a repository that could be useful for you project:

Sam Roweis also has a link to several datasets out there:

Dr. Jan Wiebe MPQA opinion annotated corpus: